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Sökning: (WFRF:(Nguyen Anh Quynh)) > Prediction of Flash...

  • Thi Thanh Ngo, HuongUniversity of Transport Technology, Hanoi, 100000, Vietnam (författare)

Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam

  • Artikel/kapitelEngelska2023

Förlag, utgivningsår, omfång ...

  • Tech Science Press,2023
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:ltu-94286
  • https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-94286URI
  • https://doi.org/10.32604/cmes.2023.022566DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • Validerad;2022;Nivå 2;2022-11-28 (hanlid);Funder: Vietnam National Foundation for Science and Technology Development (NAFOSTED) (105.08-2019.03)
  • Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were used for the development of flash flood susceptibility maps for hilly road section (115 km length) of National Highway (NH)-6 in Hoa Binh province, Vietnam. In the proposed models, 88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors. The performance of the models was evaluated using standard statistical measures including Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC) and Root Mean Square Error (RMSE). The results revealed that all the models performed well (AUC > 0.80) in predicting flash flood susceptibility zones, but the performance of the DL model is the best (AUC: 0.972, RMSE: 0.352). Therefore, the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Duc Dam, NguyenUniversity of Transport Technology, Hanoi, 100000, Vietnam (författare)
  • Thi Bui, Quynh-AnhUniversity of Transport Technology, Hanoi, 100000, Vietnam (författare)
  • Al-Ansari, Nadhir,1947-Luleå tekniska universitet,Geoteknologi(Swepub:ltu)nadhir (författare)
  • Costache, RomulusDepartment of Civil Engineering, Transilvania University of Brașov, Brasov, 500152, Romania; Danube Delta National Institute for Research and Development, Tulcea, 820112, Romania (författare)
  • Ha, HangDepartement of Geodesy and Geomatics, National University of Civil Engineering, Hanoi, 100000, Vietnam (författare)
  • Duy Bui, QuynhDepartement of Geodesy and Geomatics, National University of Civil Engineering, Hanoi, 100000, Vietnam (författare)
  • Hung Mai, SyFaculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi, 100000, Vietnam (författare)
  • Prakash, IndraDDG (R) Geological Survey of India, Gandhinagar, 382010, India (författare)
  • Thai Pham, BinhUniversity of Transport Technology, Hanoi, 100000, Vietnam (författare)
  • University of Transport Technology, Hanoi, 100000, VietnamGeoteknologi (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:CMES - Computer Modeling in Engineering & Sciences: Tech Science Press135:3, s. 2219-22411526-14921526-1506

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